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| Face Super-Resolution Reconstruction Based on Frequency-Domain Interaction and Dense Fusion |
| LIU Shu1, ZHANG Yufeng1, KUI Xiaoyan1 |
| 1. School of Computer Science and Engineering, Central South University, Changsha 410083 |
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Abstract It is difficult for existing face super-resolution methods to achieve an optimal tradeoff between computational efficiency and reconstruction quality. To address this issue, a face super-resolution network based on frequency-domain interaction and dense fusion is proposed. First, wavelet transform is adopted for frequency-domain downsampling. The spectral information of images is effectively preserved. On this basis, a global-local Transformer module is designed to serially process local window attention and global sparse attention. The overall geometric structure of the face is better captured and the computational complexity is reduced. Second, a frequency-domain interaction Transformer module is proposed to construct a cross-frequency band interaction mechanism. High-frequency features are utilized to refine low-frequency semantics, thereby improving image sharpness. Finally, cross-scale feature aggregation is realized through an adjacent cross-scale fusion mechanism. Experiments indicate that the proposed network achieves a good balance between perceptual quality and pixel fidelity while reducing the number of parameters. A new solution is provided for face super-resolution in resource-constrained scenarios. The code is available at https://github.com/Hddcc/FIDFN.
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Received: 22 January 2026
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| Fund:National Natural Science Foundation of China(No.U22A2034), Key Project of Scientific Research Fund of Hunan Provincial Education Department(No.24A0018), Key Program of Xiang-jiang Laboratory(No.23XJ02005) |
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Corresponding Authors:
KUI Xiaoyan, Ph.D., professor. Her research interests include image processing, data visualization and visual analytics.
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About author:: LIU Shu, Ph.D., associate professor. Her research interests include computer vision and multimodal intelligent cognition.
ZHANG Yufeng, Master student. His research interests include computer vision and image super-resolution. |
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2026, Vol. 39 
